1. Field of the Invention
The present invention is related to a data processing system based on the concept of a neural network.
2. Background Information
A neural network in a data processing system is constructed in a layer state by arranging neural cells in parallel, see model 1 in FIG. 3 ("neuron" hereinafter) The neuron in each layer combines by synapses with all the neurons in adjacent layers and inputs and outputs data. Concerning neuron 1, in FIG. 3, weights W1, W2, W3, . . . , Wn are multiplied by data I1, I2, I3, . . . , In inputted from outside, and data 0 is outputted corresponding to the comparison between the sum of the multiplication and threshold .theta..
Various methods are possible for the comparison. When normalized function 1[f] is adopted, output data 0 is expressed as formula (1). EQU 0=1 [.SIGMA.Wn.multidot.In-.theta.]
When .rho.Wn.multidot.In is more than or equal to threshold .theta., the neuron activates, and output data 0 is "1"; when .SIGMA.Wn.multidot.In is less than threshold .theta., the neuron is not activated, and output data 0 is "0".
Conventional neural networks have neural layers with neurons arranged in parallel with the neural layers connected in series. Neural layers are comprised of, for example, 3 layers, namely, an input layer, a middle layer and an output layer, as Perceptrons suggested by Rosenblatt. The neuron in each layer combines with all neurons in adjacent other layers by synapses.